ReviewViruses2022
Pivoting Novel Exosome-Based Technologies for the Detection of SARS-CoV-2.
Review in Viruses, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- A Cloud-Based Platform for Harmonized COVID-19 Data: Design and Implementation of the Rapid Acceleration of Diagnostics (RADx) Data Hub.JMIR public health and surveillance · 2025Article
- Extracellular RNA communication: A decade of NIH common fund support illuminates exRNA biology.Journal of extracellular vesicles · 2025Review
- Special Issue "Novel Diagnostic Technologies for SARS-CoV-2 and Other Emerging Viruses".Viruses · 2024Article
- Extracellular Vesicles: The Invisible Heroes and Villains of COVID-19 Central Neuropathology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- A state-of-the-art review of the recent advances in exosome isolation and detection methods in viral infection.Virology journal · 2024Review
- An Exosome-Based Therapeutic Strategy Targeting Neuroinflammation in Alzheimer's Disease with Berberine and Palmatine.Drug design, development and therapy · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The National Institutes of Health (NIH) launched the Rapid Acceleration of Diagnostics (RADx) initiative to meet the needs for COVID-19 diagnostic and surveillance testing, and to speed its innovation in the development, commercialization, and implementation of new technologies and approaches. The RADx Radical (RADx-Rad) initiative is one component of the NIH RADx program which focuses on the development of new or non-traditional applications of existing approaches, to enhance their usability, accessibility, and/or accuracy for the detection of SARS-CoV-2. Exosomes are a subpopulation of extracellular vesicles (EVs) 30-140 nm in size, that are critical in cell-to-cell communication. The SARS-CoV-2 virus has similar physical and molecular properties as exosomes. Therefore, the novel tools and technologies that are currently in development for the isolation and detection of exosomes, may prove to be invaluable in screening for SARS-CoV-2 viral infection. Here, we describe how novel exosome-based technologies are being pivoted for the detection of SARS-CoV-2 and/or the diagnosis of COVID-19. Considerations for these technologies as they move toward clinical validation and commercially viable diagnostics is discussed along with their future potential. Ultimately, the technologies in development under the NIH RADx-Rad exosome-based non-traditional technologies toward multi-parametric and integrated approaches for SARS-CoV-2 program represent a significant advancement in diagnostic technology, and, due to a broad focus on the biophysical and biochemical properties of nanoparticles, the technologies have the potential to be further pivoted as tools for future infectious agents.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.